Jalapeño Fund | Momentum Trading

Portfolio Value

$998,615

Total P&L

$-1,385

Total Return

-0.14%

Positions

Win Rate

Our Strategy — V5 Ultimate

Jalapeño Fund employs a quantitative, rules-based multi-factor strategy that ranks S&P 500 stocks by four academically proven factors: Momentum (35%), Quality (25%), Value (20%), and Low Volatility (20%) — each Z-scored cross-sectionally. Positions are sized using inverse-volatility weighting so lower-risk stocks get larger allocations. In high-volatility regimes, the system shifts weight toward quality and low-vol for natural downside protection.

Universe: Our system continuously analyzes all stocks within the S&P 500 index.

Ranking: We rank each security based on its risk-adjusted price momentum over the preceding 6-month period. This proprietary score identifies stocks exhibiting strong, consistent upward trends relative to their volatility.

Execution: The model portfolio invests in the top 10 highest-ranked securities with inverse-volatility position sizing (capped at 1.5× equal weight). Rebalancing occurs biweekly with 50% partial rotation. Trailing stops adapt to market volatility — tighter in turbulent markets, wider in calm ones.

Risk Management: Each position is protected by a 5x Average True Range (ATR) trailing stop to manage downside risk. Furthermore, a broad market filter is applied: we only hold long positions when the S&P 500 ETF (SPY) is trading above its 200-day moving average, helping to avoid significant market downturns.

Current Holdings

Ticker	Entry Price	Shares	Market Value	P&L	P&L %
MU	$418.01	104.9	$43836.17	$0.0	0.0%
WBD	$29.15	3572.1	$104126.81	$0.0	0.0%
TER	$329.09	162.9	$53622.08	$0.0	0.0%
CIEN	$342.7	125.6	$43054.75	$0.0	0.0%
LRCX	$244.25	224.3	$54794.8	$0.0	0.0%
GOOG	$310.92	437.2	$135919.27	$0.0	0.0%
GOOGL	$310.9	433.7	$134844.4	$0.0	0.0%
ALB	$186.83	285.6	$53352.05	$0.0	0.0%
JNJ	$246.28	609.1	$150000.0	$0.0	0.0%
ROST	$200.55	747.9	$150000.0	$0.0	0.0%

Top Momentum Picks

Ticker	Momentum Score	6M Return	Sector	Current Price
MU	3.03	258.06%	Information Technology	$418.01
WBD	2.19	111.7%	Communication Services	$29.15
TER	1.89	151.38%	Information Technology	$329.09
CIEN	1.8	162.81%	Information Technology	$342.7
LRCX	1.37	123.7%	Information Technology	$244.25
GOOG	1.25	70.26%	Communication Services	$310.92
GOOGL	1.24	70.91%	Communication Services	$310.9
ALB	1.16	127.03%	Materials	$186.83
JNJ	1.1	31.61%	Health Care	$246.28
ROST	1.02	38.88%	Consumer Discretionary	$200.55

Equity Curve

Recent Trades

Date	Ticker	Action	Price	Return %	Reason
2026-02-24	HOLX	BUY	$75.35	--	momentum_pick
2026-02-24	EA	BUY	$201.0	--	momentum_pick
2026-02-24	WBD	BUY	$29.15	--	momentum_pick
2026-02-24	MU	BUY	$418.01	--	momentum_pick
2026-02-24	GOOGL	BUY	$310.9	--	momentum_pick
2026-02-24	GOOG	BUY	$310.92	--	momentum_pick
2026-02-24	TER	BUY	$329.09	--	momentum_pick
2026-02-24	ROST	BUY	$200.55	--	momentum_pick
2026-02-24	CIEN	BUY	$342.7	--	momentum_pick
2026-02-24	LRCX	BUY	$244.25	--	momentum_pick
2026-02-24	ALB	BUY	$186.83	--	momentum_pick
2026-02-24	MNST	BUY	$85.54	--	momentum_pick
2026-02-24	BG	BUY	$122.51	--	momentum_pick
2026-02-24	NEM	BUY	$124.09	--	momentum_pick
2026-02-24	FIX	BUY	$1468.58	--	momentum_pick
2026-02-24	JNJ	BUY	$246.28	--	momentum_pick
2026-02-24	CNC	BUY	$42.37	--	momentum_pick
2026-02-24	CHRW	BUY	$177.35	--	momentum_pick
2026-02-24	STX	BUY	$396.02	--	momentum_pick
2026-02-24	MU	BUY	$418.01	--	v5_multifactor

Market Status

Market Filter (SPY > 200D MA)

Active / OK to Invest

Last S&P 500 Scan

Next Rebalance Date

🧮 The Algorithm — Full Transparency

We believe in radical transparency. Here's every signal, formula, and decision rule our algorithm uses. No black boxes. No secret sauce. Just math.

Step 1 — Universe Selection

We trade the S&P 500 — 503 of the largest, most liquid US companies. No penny stocks, no illiquid names, no crypto. Just blue chips.

                    Universe = S&P 500 constituents (AAPL, MSFT, GOOGL, ... ~503 tickers)

                    Data source = Yahoo Finance (daily OHLCV, 20+ years history)

Step 2 — Market Regime Filter

Before buying anything, we check: is this a bull or bear market? If SPY (the S&P 500 ETF) is trading below its 200-day moving average, we go 100% to cash. This single rule avoided most of the 2008 crash (-46%) and 2022 bear market.

                    MA₂₀₀(SPY) = (1/200) × Σ Close(SPY)ₜ₋ᵢ   for i = 0..199

                    IF Close(SPY)ₜ > MA₂₀₀(SPY)ₜ → RISK ON (buy momentum stocks)

                    IF Close(SPY)ₜ ≤ MA₂₀₀(SPY)ₜ → RISK OFF (sell everything, hold cash)

Step 3 — Multi-Factor Composite Score

This is the core signal. We rank every stock by four academically proven factors, each Z-scored cross-sectionally (normalized to mean 0, std 1 across all qualifying stocks). This multi-factor approach is more robust than any single signal — when momentum stumbles, quality and low-volatility pick up the slack.

                    // Factor 1: Momentum (35%) — Jegadeesh & Titman 1993

                    ret₆ₘ = Close[t-21] / Close[t-147] − 1   (6mo return, skip recent month)

                    // Factor 2: Quality (25%) — Novy-Marx 2013

                    Sortino = mean_return_ann / downside_std_ann   (126-day rolling)

                    // Factor 3: Value (20%) — Fama & French 1993

                    Value = 1 − (Close / 52-week High)   (distance from peak)

                    // Factor 4: Low Volatility (20%) — Baker et al. 2011

                    LowVol = −σ₆₀   where σ₆₀ = StdDev(daily ret, 60d) × √252

                    Composite = 0.35×Z(Mom) + 0.25×Z(Quality) + 0.20×Z(Value) + 0.20×Z(LowVol)

                    // High-volatility regime (SPY vol > 70th percentile):

                    Defensive = 0.25×Z(Mom) + 0.30×Z(Quality) + 0.20×Z(Value) + 0.25×Z(LowVol)

                    // Filters:

                    ✓ MA₅₀ > MA₂₀₀ (uptrend)   ✓ ATR% < 8%   ✓ ret₆ₘ > 0

📚 Based on: Jegadeesh & Titman (1993), Fama & French (1993), Novy-Marx (2013), Baker, Bradley & Wurgler (2011)

Step 4 — Portfolio Construction

We buy the top 10 ranked stocks with inverse-volatility sizing. Lower-volatility stocks get larger positions (risk parity). Each position is capped at 1.5× equal weight to prevent over-concentration. Every 10 trading days, we sell the worst 50% and replace with fresh top-ranked picks.

                    Top N = 10 positions (inverse-vol weighted)

                    weight_i = (1/σᵢ) / Σ(1/σⱼ) × Available Capital

                    Max weight per position = 1.5 × (Portfolio / 10) = 15% cap

                    Rebalance frequency = every 10 trading days (~2 weeks)

                    Rotation % = 50% (sell bottom half, keep top half)

                    SELL bottom 50% of holdings by return since entry

                    SELL any holding that dropped out of top 40 rankings

                    BUY highest-ranked stocks not already held

                    // Cash from sales funds new positions

Step 5 — Risk Management (Trailing Stops)

Every position gets a trailing stop loss based on ATR (Average True Range) — a volatility-adaptive measure. The stop follows the stock up but never down. If a stock drops 5× its ATR from its peak, we exit immediately. No exceptions, no emotions.

                    // True Range (measures daily volatility)

                    TR = max(High - Low, |High - Closeₜ₋₁|, |Low - Closeₜ₋₁|)

                    // Average True Range (14-day simple average)

                    ATR₁₄ = (1/14) × Σ TRₜ₋ᵢ   for i = 0..13

                    // Trailing stop (follows price UP, never down)

                    Stop = Peak Price − (5.0 × ATR₁₄)

                    IF Closeₜ ≤ Stop → SELL IMMEDIATELY

                    // Example: Stock peaks at $100, ATR = $2

                    // Stop = $100 - (5 × $2) = $90

                    // Allows 10% drawdown before exit

Step 6 — Transaction Cost Modeling

We model 0.30% round-trip costs (0.15% per trade) — conservative for retail. This includes broker commissions, bid-ask spread, and market impact. We also deduct 1.5% annually for survivorship bias (we're using today's S&P 500 members historically, which slightly inflates returns).

                    Commission = 0.15% per trade (buy or sell)

                    Round-trip cost = 0.30% per position

                    Survivorship bias drag = 1.5% / year

                    // Deducted daily: (1 - 0.015)^(1/252) per day

                    After-tax drag = additional 2.0% / year (short-term capital gains)

                    Net Return = Gross Return − Commissions − Survivorship Drag − Tax Drag

📊 V5 Backtest Results — 20 Years (2005–2026)

Here's what the algorithm produced over 20 years, after all costs and adjustments:

15.57%

Annualized Return (net)

0.93

Sharpe Ratio

-23.19%

Max Drawdown

5,156

Total Trades

                    // Crisis Performance (our real edge):

                    2008 GFC: Strategy -1.51% vs SPY -46.56% → +45.05% excess

                    2020 COVID: Strategy +21.47% vs SPY +16.26%

                    2022 Bear: Strategy -9.28% vs SPY -19.44% → +10.16% excess

                    The edge isn't beating SPY in bull markets — it's SURVIVING bear markets.

                    Half the drawdown for similar long-term returns.

⚖️ Honest Assessment

Over the full 20-year period, this strategy beats SPY by +4.7% annually after costs (15.57% vs 10.84%). It combines four factor premia into one composite score, reducing reliance on any single anomaly. The real value is risk management: the market filter and trailing stops cut maximum drawdown roughly in half. Monte Carlo simulation (10,000 bootstraps) gives a approximately 70% probability of beating SPY over any given period. This is not a get-rich-quick scheme — it's a disciplined, systematic approach to capturing momentum with downside protection.